Microsimulation and Lifetable Modelling

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Ali Abbas

MRC-Epidemiology Unit, University of Cambridge

July 7, 2023

Structure

Agenda

  • Why Modelling?
  • Microsimulation Modelling
  • Lifetable Modelling
  • Summary

Modelling

Why Modelling? (1/2)

  • Simple representation of complex world/phenomena
  • Experiments are not possible or feasible (e.g. randomised control trials)
  • Explore alternative consequences using scenarios
  • Trials are short (intermediate end-points), need for extrapolation Cholesterol levels (trial) to heart disease (model)
  • Generalisable to other settings (age groups, country)
  • Synthesizing data from various sources
  • Informing decisions in the absence of hard data (recent pandemic)

Why Modelling? (2/2)

Courtesy: Randall Monroe, xkcd: <https://xkcd.com/1838>

Microsimulation

Definition

  • Modelling technique operates at individual level (such as persons, households, vehicles, or firms)

  • Estimates how demographic, behavioral, and policy changes might affect individual outcomes; also

  • To better understand the effects of current policies

  • | |

Example - Transport PM 2.5 for Munich

How it differs from traditional models?

  • Individual level (such as persons, households, vehicles, or firms)

  • Autonomous entity - with limited learning/adaptability abilities

  • Local interactions impact macro/aggregate levels

  • Explorative that can be used for explanation or prediction

  • Resource intensive - even with sample population

  • Nuanced understanding

Ingredients

  • Synthetic Population1

  • Social/physical space

  • Rules of engagement with other actors/environment

Synthetic Population

  • Census population

  • Localized prediction model based on the most recent Census

  • Surveys (like a travel or Physical Activity Survey)

Examples - using Bogota (1/2)

Examples - using Bogota (2/2)

Tutorial paper

Krijkamp, Eline M., et al. “Microsimulation modeling for health decision sciences using R: a tutorial.” Medical Decision Making 38.3 (2018): 400-422

Lifetable Modelling

Definition - Lifetable Modelling

  • Two states model: alive and dead
  • Outcomes: life years, and life expectancy.

Input/Outputs

  • Inputs: Mortality rates
  • Depicts: Life expectancy at different ages
  • Period life tables: individuals exposed over hypothetical life course to mortality rates observed in one calendar year
  • Projected mortality rates: Simulation relies on cohort life tables

Source of Input Datasets

  • All-cause mortality rate: National Statistics offices/Global Burden of Disease Study

  • All-cause mortality rate trends: National Statistics offices

  • Population for each cohort (can also be 100,000 or similar figure)

Two-state lifetable model (1/3)

Two-state lifetable model (2/3)

Two-state lifetable model (3/3)

  • Average life years without intervention: 6,362

  • Average life years without intervention: 7,005

  • Life years gained: 643 (7,005 - 6,362)

  • Caveat

    • Probability of dying increases with age

    • Morbidity is captured by proportional multi-state lifetable models

Summary

  • Microsimulation

    • Pros:

      • Large state spaces and captures diversity/heterogeneity on number of variables

      • May incorporate expert opinions

    • Cons:

      • Technologically challenging and resource intensive

      • (Yet) unsupported assumptions

  • Lifetable Modelling

    • Pros

      • Relatively easy to set up

      • Comparatively low data requirements and easier to justify using Cost Benefit Analysis

    • Cons

      • Only mortality modelling

      • Typically based on annual rates

References

  1. Veerman JL, Barendregt JJ,Mackenbach JP Quantitative health impact assessment: current practice and future directions Journal of Epidemiology & Community Health 2005;59:361-370.
  2. Briggs, A.D.M., Wolstenholme, J., Blakely, T. et al. Choosing an epidemiological model structure for the economic evaluation of non-communicable disease public health interventions. Popul Health Metrics 14, 17 (2016). https://doi.org/10.1186/s12963-016-0085-1
  3. Blakely, T., et al. (2020). Multistate lifetable modelling of preventive interventions: concepts, code and worked examples.
  4. Barendregt, J. J., et al. (1998). “Coping with multiple morbidity in a life table.” Math Popul Stud 7(1): 29-49.
  5. Moeckel, R., Spiekermann, K., & Wegener, M. (2003, May). Creating a synthetic population. In Proceedings of the 8th international conference on computers in urban planning and urban management (CUPUM) (pp. 1-18).

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